{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "d44fbc04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "select dwd_prj_count_sub.*,ods_prj_tgprjlist.project_year,ods_prj_tgprjlist.project_addr addr,\n",
      "IFNULL(bus_web_city.id,430000) city_id,voltage_classes\n",
      ",CASE dwd_prj_count_sub.project_type WHEN  '变电站' THEN 1 WHEN  '通信站' THEN 2 else 3 end t\n",
      ",CASE voltage_classes WHEN  '交流110kV' THEN 110 WHEN  '交流220kV' THEN 220 WHEN  '交流35kV' THEN 350 else 550 end classes\n",
      "from dwd_prj_count_sub left join ods_prj_tgprjlist on ods_prj_tgprjlist.project_id =\n",
      "dwd_prj_count_sub.project_id left join bus_web_city on bus_web_city.name \n",
      "= concat(ods_prj_tgprjlist.project_addr,'市') where 1=1 -- bus_web_city.id ='430100'\n",
      " -- ods_prj_tgprjlist.project_year ='2019' and voltage_classes='交流110kV'\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import pymysql\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "sql_query = '''select dwd_prj_count_sub.*,ods_prj_tgprjlist.project_year,ods_prj_tgprjlist.project_addr addr,\n",
    "IFNULL(bus_web_city.id,430000) city_id,voltage_classes\n",
    ",CASE dwd_prj_count_sub.project_type WHEN  '变电站' THEN 1 WHEN  '通信站' THEN 2 else 3 end t\n",
    ",CASE voltage_classes WHEN  '交流110kV' THEN 110 WHEN  '交流220kV' THEN 220 WHEN  '交流35kV' THEN 350 else 550 end classes\n",
    "from dwd_prj_count_sub left join ods_prj_tgprjlist on ods_prj_tgprjlist.project_id =\n",
    "dwd_prj_count_sub.project_id left join bus_web_city on bus_web_city.name \n",
    "= concat(ods_prj_tgprjlist.project_addr,'市') where 1=1 -- bus_web_city.id ='430100'\n",
    " -- ods_prj_tgprjlist.project_year ='2019' and voltage_classes='交流110kV'\n",
    "''' \n",
    "print(sql_query)\n",
    "engine = create_engine('mysql+pymysql://root:123456@localhost:3306/buildcost')\n",
    "data = pd.read_sql_query(sql_query, engine)\n",
    "# print(data.iloc[0])\n",
    "# data.columns = data.iloc[0]\n",
    "# print(data.info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "500dd7c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-233-1ebf31b1c997>:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['gy'] = (data1['gs_jzgcf'].astype(float))/data1['ys_jzgcf'].astype(float)\n",
      "<ipython-input-233-1ebf31b1c997>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['yj'] = (data1['ys_jzgcf'].astype(float))/data1['js_jzgcf'].astype(float)\n",
      "<ipython-input-233-1ebf31b1c997>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['yj_sbgzf'] = (data1['ys_sbgzf'].astype(float))/data1['js_sbgzf'].astype(float)\n",
      "<ipython-input-233-1ebf31b1c997>:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['yj_azgcf'] = (data1['ys_azgcf'].astype(float))/data1['js_azgcf'].astype(float)\n",
      "<ipython-input-233-1ebf31b1c997>:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['yj_qtfy'] = (data1['ys_qtfy'].astype(float))/data1['js_qtfy'].astype(float)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>yj</td>        <th>  R-squared:         </th> <td>   0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   8.917</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 29 Apr 2021</td> <th>  Prob (F-statistic):</th> <td>0.000166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:47:26</td>     <th>  Log-Likelihood:    </th> <td>  366.49</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   365</td>      <th>  AIC:               </th> <td>  -727.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   362</td>      <th>  BIC:               </th> <td>  -715.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_id</th>      <td>  3.33e-05</td> <td> 1.18e-05</td> <td>    2.813</td> <td> 0.005</td> <td>    1e-05</td> <td> 5.66e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>project_year</th> <td>   -0.0274</td> <td>    0.009</td> <td>   -2.929</td> <td> 0.004</td> <td>   -0.046</td> <td>   -0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>t</th>            <td>   42.0326</td> <td>   19.936</td> <td>    2.108</td> <td> 0.036</td> <td>    2.828</td> <td>   81.238</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>102.512</td> <th>  Durbin-Watson:     </th> <td>   2.120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>5019.645</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.017</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>21.167</td>  <th>  Cond. No.          </th> <td>1.84e+09</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.84e+09. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     yj   R-squared:                       0.047\n",
       "Model:                            OLS   Adj. R-squared:                  0.042\n",
       "Method:                 Least Squares   F-statistic:                     8.917\n",
       "Date:                Thu, 29 Apr 2021   Prob (F-statistic):           0.000166\n",
       "Time:                        15:47:26   Log-Likelihood:                 366.49\n",
       "No. Observations:                 365   AIC:                            -727.0\n",
       "Df Residuals:                     362   BIC:                            -715.3\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "city_id        3.33e-05   1.18e-05      2.813      0.005       1e-05    5.66e-05\n",
       "project_year    -0.0274      0.009     -2.929      0.004      -0.046      -0.009\n",
       "t               42.0326     19.936      2.108      0.036       2.828      81.238\n",
       "==============================================================================\n",
       "Omnibus:                      102.512   Durbin-Watson:                   2.120\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             5019.645\n",
       "Skew:                           0.017   Prob(JB):                         0.00\n",
       "Kurtosis:                      21.167   Cond. No.                     1.84e+09\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.84e+09. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#可以通过subset参数来删除在gg和add中含有空数据的全部行\n",
    "data1 = data.dropna(subset=['city_id', 't', 'ys_jzgcf','js_jzgcf','project_year','voltage_classes'])\n",
    "# data1['gy'] = (data1['gs_jzgcf'].astype(float)-data1['ys_jzgcf'].astype(float))/data1['ys_jzgcf'].astype(float)\n",
    "# data1['yj'] = (data1['ys_jzgcf'].astype(float)-data1['js_jzgcf'].astype(float))/data1['js_jzgcf'].astype(float)\n",
    "# data1['yj_sbgzf'] = (data1['ys_sbgzf'].astype(float)-data1['js_sbgzf'].astype(float))/data1['js_sbgzf'].astype(float)\n",
    "# data1['yj_azgcf'] = (data1['ys_azgcf'].astype(float)-data1['js_azgcf'].astype(float))/data1['js_azgcf'].astype(float)\n",
    "# data1['yj_qtfy'] = (data1['ys_qtfy'].astype(float)-data1['js_qtfy'].astype(float))/data1['js_qtfy'].astype(float)\n",
    "\n",
    "data1['gy'] = (data1['gs_jzgcf'].astype(float))/data1['ys_jzgcf'].astype(float)\n",
    "data1['yj'] = (data1['ys_jzgcf'].astype(float))/data1['js_jzgcf'].astype(float)\n",
    "data1['yj_sbgzf'] = (data1['ys_sbgzf'].astype(float))/data1['js_sbgzf'].astype(float)\n",
    "data1['yj_azgcf'] = (data1['ys_azgcf'].astype(float))/data1['js_azgcf'].astype(float)\n",
    "data1['yj_qtfy'] = (data1['ys_qtfy'].astype(float))/data1['js_qtfy'].astype(float)\n",
    "x = sm.add_constant(data1[['city_id','project_year','t']]) #生成自变量\n",
    "# print(x)\n",
    "# # x= data1['project_addr'] \n",
    "y = data1['yj'] #生成因变量\n",
    "# # print(np.asarray(y))\n",
    "model = sm.OLS(y, x.astype(float)) #生成模型\n",
    "result = model.fit() #模型拟合\n",
    "result.summary() #模型描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "1bb1e9ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x210aac599a0>"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 中文支持\n",
    "plt.rcParams['savefig.dpi'] = 600 #图片像素\n",
    "plt.rcParams['figure.dpi'] = 300 #分辨率\n",
    "plt.scatter(data1['city_id']*1.00003, data1['yj'], marker='.',c='r')\n",
    "plt.scatter(data1['city_id']*1.00006, data1['yj_sbgzf'], marker='.',c='b')\n",
    "plt.scatter(data1['city_id']*1.00009, data1['yj_azgcf'], marker='.',c='y')\n",
    "plt.scatter(data1['city_id'], data1['yj_qtfy'], marker='.',c='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "11c8e0fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x210aac5dc40>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data1['project_year'].astype(float)*1.00003, data1['yj_sbgzf'], marker='.',c='b')\n",
    "plt.scatter(data1['project_year'].astype(float)*1.00006, data1['yj_azgcf'], marker='.',c='y')\n",
    "plt.scatter(data1['project_year'].astype(float)*1.00009, data1['yj_qtfy'], marker='.',c='g')\n",
    "plt.scatter(data1['project_year'].astype(float), data1['yj'], marker='.',c='r')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "48a26d5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x210b4be27f0>"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.scatter(data1['project_type'], data1['yj'], marker='.')\n",
    "plt.scatter(data1['classes'], data1['yj_sbgzf'], marker='.',c='b')\n",
    "plt.scatter(data1['classes']+5, data1['yj_azgcf'], marker='.',c='y')\n",
    "plt.scatter(data1['classes']+3, data1['yj_qtfy'], marker='.',c='g')\n",
    "plt.scatter(data1['classes']+8, data1['yj'], marker='.',c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "0db1e40b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 df      sum_sq   mean_sq         F    PR(>F)\n",
      "classes         1.0    5.034078  5.034078  2.521831  0.113156\n",
      "city_id         1.0    6.663994  6.663994  3.338341  0.068508\n",
      "project_year    1.0    0.552653  0.552653  0.276853  0.599095\n",
      "Residual      361.0  720.627986  1.996199       NaN       NaN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-239-c7b10758f265>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['project_year']=data1['project_year'].astype(float)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>yj_azgcf</td>     <th>  R-squared:         </th> <td>   0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 29 Apr 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.107</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:08:31</td>     <th>  Log-Likelihood:    </th> <td> -642.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   365</td>      <th>  AIC:               </th> <td>   1292.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   361</td>      <th>  BIC:               </th> <td>   1308.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>  -10.1669</td> <td>  316.487</td> <td>   -0.032</td> <td> 0.974</td> <td> -632.557</td> <td>  612.223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_id</th>      <td>   -0.0003</td> <td>    0.000</td> <td>   -1.782</td> <td> 0.076</td> <td>   -0.001</td> <td> 3.47e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>project_year</th> <td>    0.0781</td> <td>    0.149</td> <td>    0.526</td> <td> 0.599</td> <td>   -0.214</td> <td>    0.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>classes</th>      <td>   -0.0010</td> <td>    0.001</td> <td>   -1.593</td> <td> 0.112</td> <td>   -0.002</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>198.818</td> <th>  Durbin-Watson:     </th> <td>   1.925</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>2100.663</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 2.058</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>14.008</td>  <th>  Cond. No.          </th> <td>1.84e+09</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.84e+09. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               yj_azgcf   R-squared:                       0.017\n",
       "Model:                            OLS   Adj. R-squared:                  0.009\n",
       "Method:                 Least Squares   F-statistic:                     2.046\n",
       "Date:                Thu, 29 Apr 2021   Prob (F-statistic):              0.107\n",
       "Time:                        16:08:31   Log-Likelihood:                -642.05\n",
       "No. Observations:                 365   AIC:                             1292.\n",
       "Df Residuals:                     361   BIC:                             1308.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const          -10.1669    316.487     -0.032      0.974    -632.557     612.223\n",
       "city_id         -0.0003      0.000     -1.782      0.076      -0.001    3.47e-05\n",
       "project_year     0.0781      0.149      0.526      0.599      -0.214       0.370\n",
       "classes         -0.0010      0.001     -1.593      0.112      -0.002       0.000\n",
       "==============================================================================\n",
       "Omnibus:                      198.818   Durbin-Watson:                   1.925\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2100.663\n",
       "Skew:                           2.058   Prob(JB):                         0.00\n",
       "Kurtosis:                      14.008   Cond. No.                     1.84e+09\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.84e+09. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "data1['project_year']=data1['project_year'].astype(float)\n",
    "x = sm.add_constant(data1[['city_id','project_year','classes']]) #生成自变量\n",
    "formula = 'yj_azgcf~ classes + city_id + project_year'\n",
    "anova_results = anova_lm(ols(formula,data1).fit())\n",
    "print(anova_results)# 安装工程\n",
    "y = data1['yj_azgcf'] #生成因变量\n",
    "# # print(np.asarray(y))\n",
    "model = sm.OLS(y, x.astype(float)) #生成模型\n",
    "result = model.fit() #模型拟合\n",
    "result.summary() #模型描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "6c3ecbd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 df    sum_sq   mean_sq          F    PR(>F)\n",
      "classes         1.0  0.093639  0.093639  12.189111  0.000540\n",
      "city_id         1.0  0.071979  0.071979   9.369676  0.002371\n",
      "project_year    1.0  0.071108  0.071108   9.256182  0.002519\n",
      "Residual      361.0  2.773265  0.007682        NaN       NaN\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>yj</td>        <th>  R-squared:         </th> <td>   0.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   10.27</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 29 Apr 2021</td> <th>  Prob (F-statistic):</th> <td>1.67e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:09:27</td>     <th>  Log-Likelihood:    </th> <td>  372.66</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   365</td>      <th>  AIC:               </th> <td>  -737.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   361</td>      <th>  BIC:               </th> <td>  -721.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>   43.5134</td> <td>   19.633</td> <td>    2.216</td> <td> 0.027</td> <td>    4.903</td> <td>   82.124</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_id</th>      <td>  3.29e-05</td> <td> 1.17e-05</td> <td>    2.822</td> <td> 0.005</td> <td> 9.97e-06</td> <td> 5.58e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>project_year</th> <td>   -0.0280</td> <td>    0.009</td> <td>   -3.042</td> <td> 0.003</td> <td>   -0.046</td> <td>   -0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>classes</th>      <td>   -0.0001</td> <td> 3.86e-05</td> <td>   -3.524</td> <td> 0.000</td> <td>   -0.000</td> <td>   -6e-05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>97.388</td> <th>  Durbin-Watson:     </th> <td>   2.114</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>3883.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.099</td> <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>18.978</td> <th>  Cond. No.          </th> <td>1.84e+09</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.84e+09. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     yj   R-squared:                       0.079\n",
       "Model:                            OLS   Adj. R-squared:                  0.071\n",
       "Method:                 Least Squares   F-statistic:                     10.27\n",
       "Date:                Thu, 29 Apr 2021   Prob (F-statistic):           1.67e-06\n",
       "Time:                        16:09:27   Log-Likelihood:                 372.66\n",
       "No. Observations:                 365   AIC:                            -737.3\n",
       "Df Residuals:                     361   BIC:                            -721.7\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const           43.5134     19.633      2.216      0.027       4.903      82.124\n",
       "city_id        3.29e-05   1.17e-05      2.822      0.005    9.97e-06    5.58e-05\n",
       "project_year    -0.0280      0.009     -3.042      0.003      -0.046      -0.010\n",
       "classes         -0.0001   3.86e-05     -3.524      0.000      -0.000      -6e-05\n",
       "==============================================================================\n",
       "Omnibus:                       97.388   Durbin-Watson:                   2.114\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3883.022\n",
       "Skew:                           0.099   Prob(JB):                         0.00\n",
       "Kurtosis:                      18.978   Cond. No.                     1.84e+09\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.84e+09. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = 'yj ~ classes + city_id + project_year'\n",
    "anova_results = anova_lm(ols(formula,data1).fit())\n",
    "print(anova_results) #建筑工程\n",
    "y = data1['yj'] #生成因变量\n",
    "model = sm.OLS(y, x.astype(float)) #生成模型\n",
    "result = model.fit() #模型拟合\n",
    "result.summary() #模型描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "dac36379",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 df     sum_sq   mean_sq         F    PR(>F)\n",
      "classes         1.0   0.015820  0.015820  0.096901  0.755761\n",
      "city_id         1.0   0.103711  0.103711  0.635239  0.425964\n",
      "project_year    1.0   0.067086  0.067086  0.410907  0.521916\n",
      "Residual      361.0  58.937714  0.163262       NaN       NaN\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>yj_sbgzf</td>     <th>  R-squared:         </th> <td>   0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.3810</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 29 Apr 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.767</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:09:35</td>     <th>  Log-Likelihood:    </th> <td> -185.14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   365</td>      <th>  AIC:               </th> <td>   378.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   361</td>      <th>  BIC:               </th> <td>   393.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>  -74.0700</td> <td>   90.510</td> <td>   -0.818</td> <td> 0.414</td> <td> -252.063</td> <td>  103.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_id</th>      <td> 4.532e-05</td> <td> 5.37e-05</td> <td>    0.843</td> <td> 0.400</td> <td>-6.04e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>project_year</th> <td>    0.0272</td> <td>    0.042</td> <td>    0.641</td> <td> 0.522</td> <td>   -0.056</td> <td>    0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>classes</th>      <td>-5.191e-05</td> <td>    0.000</td> <td>   -0.292</td> <td> 0.770</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>745.905</td> <th>  Durbin-Watson:     </th>  <td>   1.994</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>826030.410</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>13.831</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>234.407</td> <th>  Cond. No.          </th>  <td>1.84e+09</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.84e+09. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               yj_sbgzf   R-squared:                       0.003\n",
       "Model:                            OLS   Adj. R-squared:                 -0.005\n",
       "Method:                 Least Squares   F-statistic:                    0.3810\n",
       "Date:                Thu, 29 Apr 2021   Prob (F-statistic):              0.767\n",
       "Time:                        16:09:35   Log-Likelihood:                -185.14\n",
       "No. Observations:                 365   AIC:                             378.3\n",
       "Df Residuals:                     361   BIC:                             393.9\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const          -74.0700     90.510     -0.818      0.414    -252.063     103.923\n",
       "city_id       4.532e-05   5.37e-05      0.843      0.400   -6.04e-05       0.000\n",
       "project_year     0.0272      0.042      0.641      0.522      -0.056       0.111\n",
       "classes      -5.191e-05      0.000     -0.292      0.770      -0.000       0.000\n",
       "==============================================================================\n",
       "Omnibus:                      745.905   Durbin-Watson:                   1.994\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           826030.410\n",
       "Skew:                          13.831   Prob(JB):                         0.00\n",
       "Kurtosis:                     234.407   Cond. No.                     1.84e+09\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.84e+09. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = 'yj_sbgzf ~ classes + city_id + project_year'\n",
    "anova_results = anova_lm(ols(formula,data1).fit())\n",
    "print(anova_results) # 设备\n",
    "y = data1['yj_sbgzf'] #生成因变量\n",
    "model = sm.OLS(y, x.astype(float)) #生成模型\n",
    "result = model.fit() #模型拟合\n",
    "result.summary() #模型描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "ccb04dd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 df    sum_sq   mean_sq          F    PR(>F)\n",
      "classes         1.0  0.234224  0.234224  19.808791  0.000011\n",
      "city_id         1.0  0.023477  0.023477   1.985533  0.159669\n",
      "project_year    1.0  0.005426  0.005426   0.458909  0.498568\n",
      "Residual      361.0  4.268547  0.011824        NaN       NaN\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>yj_qtfy</td>     <th>  R-squared:         </th> <td>   0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   7.418</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 29 Apr 2021</td> <th>  Prob (F-statistic):</th> <td>7.81e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:09:44</td>     <th>  Log-Likelihood:    </th> <td>  293.96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   365</td>      <th>  AIC:               </th> <td>  -579.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   361</td>      <th>  BIC:               </th> <td>  -564.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>    8.3628</td> <td>   24.358</td> <td>    0.343</td> <td> 0.732</td> <td>  -39.538</td> <td>   56.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_id</th>      <td> 1.958e-05</td> <td> 1.45e-05</td> <td>    1.354</td> <td> 0.177</td> <td>-8.86e-06</td> <td>  4.8e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>project_year</th> <td>   -0.0077</td> <td>    0.011</td> <td>   -0.677</td> <td> 0.499</td> <td>   -0.030</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>classes</th>      <td>    0.0002</td> <td> 4.78e-05</td> <td>    4.448</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>31.624</td> <th>  Durbin-Watson:     </th> <td>   1.400</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 140.296</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.083</td> <th>  Prob(JB):          </th> <td>3.43e-31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.033</td> <th>  Cond. No.          </th> <td>1.84e+09</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.84e+09. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                yj_qtfy   R-squared:                       0.058\n",
       "Model:                            OLS   Adj. R-squared:                  0.050\n",
       "Method:                 Least Squares   F-statistic:                     7.418\n",
       "Date:                Thu, 29 Apr 2021   Prob (F-statistic):           7.81e-05\n",
       "Time:                        16:09:44   Log-Likelihood:                 293.96\n",
       "No. Observations:                 365   AIC:                            -579.9\n",
       "Df Residuals:                     361   BIC:                            -564.3\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const            8.3628     24.358      0.343      0.732     -39.538      56.264\n",
       "city_id       1.958e-05   1.45e-05      1.354      0.177   -8.86e-06     4.8e-05\n",
       "project_year    -0.0077      0.011     -0.677      0.499      -0.030       0.015\n",
       "classes          0.0002   4.78e-05      4.448      0.000       0.000       0.000\n",
       "==============================================================================\n",
       "Omnibus:                       31.624   Durbin-Watson:                   1.400\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              140.296\n",
       "Skew:                           0.083   Prob(JB):                     3.43e-31\n",
       "Kurtosis:                       6.033   Cond. No.                     1.84e+09\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.84e+09. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = 'yj_qtfy ~ classes + city_id + project_year'\n",
    "anova_results = anova_lm(ols(formula,data1).fit())\n",
    "print(anova_results) # 其他\n",
    "y = data1['yj_qtfy'] #生成因变量\n",
    "model = sm.OLS(y, x.astype(float)) #生成模型\n",
    "result = model.fit() #模型拟合\n",
    "result.summary() #模型描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67b79d04",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py8",
   "language": "python",
   "name": "py8"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
